This topic compares two contemporary large language model releases (GPT-5.2 and Claude Opus 4.5) through the lens of technical, mathematical and scientific use cases: symbolic reasoning, numerical accuracy, reproducibility, and tool integration. With no related articles supplied here, the overview synthesizes available product and platform descriptions and observable 2025 trends: teams now prioritize models’ stepwise reasoning, callable toolchains (solvers, calculators, code execution), retrieval-augmented generation for source grounding, and clear outputs that support verification and reproducible workflows. Key considerations for choosing between these models include math/formal-reasoning fidelity, support for external tools and APIs, context-window size for long technical documents, and model behavior under chain-of-thought or constrained prompting. Supporting tools and platforms play complementary roles: enterprise copilots and content platforms (e.g., Jasper, Writesonic, Copy.ai, Rytr) focus on marketing and writer productivity; QuillBot offers paraphrasing, grammar and summarization utilities that are useful for editing technical prose; and broad-access services (e.g., ChatGPT-facing sites) often gate features or tiers behind JS/cookie flows and tiered pricing, affecting availability for heavy technical workloads. Practical guidance emphasized here is empirical: run task-specific benchmarks (proof checking, symbolic algebra, numerics, code generation + execution), verify outputs with external solvers, and prefer models with robust tool integrations and audit logs for scientific reproducibility. This comparison framework helps technical teams select and validate whichever LLM—GPT-5.2 or Claude Opus 4.5—best meets the accuracy, traceability, and integration needs of their scientific and mathematical workflows.